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by David E. Kieras, David E. Kieras, David E. Meyer, David E. Meyer
ftp://ftp.eecs.umich.edu/people/kieras/EPIC/TR-EPIC-11.pdf
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Abstract:
Predictive modeling of human performance as long been applied in human factors engineering. In the meantime, computational cognitive architecture models have developed a theoretically coherent and sophisticated basis for advanced predictive modeling of human performance. Key is a distinction between fixed task-independent architectural mechanisms and task-specific strategies that control the architecture. Applying the new cognitive modeling approaches to system design problems requires a method for identifying the task strategy, but new results suggest that humans choose task strategies that incorporate optional features that are not based on either architectural constraints or task demands. These optional features strongly influence performance, but cannot be identified by conventional task analysis methods. A heuristic is presented for obtained useful task performance predictions based on characterizing the fast-possible and slowest-reasonable combinations of optional task strategy aspects. The heuristic is illustrated with results from a somewhat complex laboratory task.
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